Background and purpose: Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardiopulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. Materials and methods: The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability. Results: The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95-0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring. Conclusion: The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.

No abstract

Purpose Manual delineation of head and neck (H&N) organ‐at‐risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multiatlas selection‐based approach for fast and reproducible segmentation. Methods Our approach dynamically selects and weights the appropriate number of atlases for weighted label fusion and generates segmentations and consensus maps indicating voxel‐wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image level and called global weighted voting (GWV) or at the structure level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of computed tomography (CT)‐radiodensity and modality‐independent neighborhood descriptors (extracting edge information). Performance comparisons were performed using 77 H&N CT images from an internal Memorial Sloan‐Kettering Cancer Center dataset (N = 45) and an external dataset (N = 32) using Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, median of maximum surface distance, and volume ratio error against expert delineation. Pairwise DSC accuracy comparisons of proposed (GWV, SWV) vs single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank‐sum tests. Results Both SWV and GWV methods produced significantly better segmentation accuracy than BA (P < 0.001) and MV (P < 0.001) for all OARs within both datasets. SWV generated the most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV’s accuracy exceeded GWV's for submandibular glands (DSC = 0.60 vs 0.52, P = 0.019). Conclusions The contributed SWV and GWV methods generated more accurate automated segmentations than the other two multiatlas‐based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel‐wise consensus between atlases within OARs during manual review.

This study presents the application of a simplex active surface model featuring weak shape priors for 3D segmentation of healthy as well as herniated discs. A framework was developed that enables the application of shape priors in the healthy part of disc anatomy, with user intervention when the priors were inapplicable. The surface-mesh-based segmentation method is part of a processing pipeline for anatomical modelling to support interactive surgery simulation.

Keywords:Human Resource Management, Knowledge Sharing, Performance Appraisals, Compensation, Training. HRM is considered as a backbone for successful organizations and knowledge sharing plays a vital role for an organization to success globally. The purpose of the study is to understand the factors that contribute to knowledge sharing in an organization. This study explains how the effective training, fair compensation and performance appraisal affects the knowledge sharing. The impact of trust on relationship of HR practices and knowledge sharing is also examined. The study empirically analyzes the relationship between HR practices and the knowledge sharing and the strong effect of trust. The correlations, simple and multiple regression tests apply to data. Findings provide evidence of a positive and significant relationship between the HR practices and knowledge sharing in the presence of trust. An organization that wants to enhance the creation, innovation and development of knowledge sharing in organization must pay attention to its HRM practices and on trust worthy environment.